Classification of ROI-based fMRI data in short-term memory tasks using discriminant analysis and neural networks.

Understanding brain function relies on identifying spatiotemporal patterns in brain activity. In recent years, machine learning methods have been widely used to detect connections between regions of interest (ROIs) involved in cognitive functions, as measured by the fMRI technique. However, it's essential to match the type of learning method to the problem type, and extracting the information about the most important ROI connections might be challenging.

Dynamics of working memory process revealed by independent component analysis in an fMRI study.

Human memory is prone to errors in many everyday activities but also when cultivating hobbies such as traveling and/or learning a new language. For instance, while visiting foreign countries, people erroneously recall foreign language words that are meaningless to them. Our research simulated such errors in a modified Deese-Roediger-McDermott paradigm for short-term memory with phonologically related stimuli aimed at uncovering behavioral and neuronal indices of false memory formation with regard to time-of-day, a variable known to influence memory.

Neural spatio-temporal patterns of information processing related to cognitive conflict and correct or false recognitions.

Using a visual short-term memory task and employing a new methodological approach, we analyzed neural responses from the perspective of the conflict level and correctness/erroneous over a longer time window. Sixty-five participants performed the short-term memory task in the fMRI scanner. We explore neural spatio-temporal patterns of information processing in the context of correct or erroneous response and high or low level of cognitive conflict using classical fMRI analysis, surface-based cortical data, temporal analysis of interpolated mean activations, and machine learning classifiers.

Neuroimaging of chronotype, sleep quality and daytime sleepiness: Structural T1-weighted magnetic resonance brain imaging data from 136 young adults.

The dataset contains structural T1-weighted magnetic resonance brain imaging data from 136 young individuals (87 females; age range from 18 to 35 years old) along with questionnaire-assessed measurements of trait-like chronotype, sleep quality and daytime sleepiness. The recruitment criteria excluded individuals with self-reported history of psychiatric or neurological conditions and current medication use. All the brain imaging sessions were performed between 5:20 PM and 8:55 PM in order to control the effect of time of day on acquired images.

Analysis of fMRI Signals from Working Memory Tasks and Resting-State of Brain: Neutrosophic-Entropy-Based Clustering Algorithm.

This study applies a neutrosophic-entropy-based clustering algorithm (NEBCA) to analyze the fMRI signals. We consider the data obtained from four different working memory tasks and the brain's resting state for the experimental purpose. Three non-overlapping clusters of data related to temporal brain activity are determined and statistically analyzed.

Non-linear Functional Brain Co-activations in Short-Term Memory Distortion Tasks.

Recent works shed light on the neural correlates of true and false recognition and the influence of time of day on cognitive performance. The current study aimed to investigate the modulation of the false memory formation by the time of day using a non-linear correlation analysis originally designed for fMRI resting-state data. Fifty-four young and healthy participants (32 females, mean age: 24.

Time-of-day effects on objective and subjective short-term memory task performance.

The time-of-day along with the synchrony effect (better performance at optimal times of the day according to the chronotype) on the cognitive performance has been well established in previous research. This influence is mediated by both circadian and homeostatic processes consistent with the Borbély two-process model. This experiment focused on the objective and subjective performance of the visual short-term memory task requiring holistic processing.

Identifying Diurnal Variability of Brain Connectivity Patterns Using Graph Theory.

Significant differences exist in human brain functions affected by time of day and by people's diurnal preferences (chronotypes) that are rarely considered in brain studies. In the current study, using network neuroscience and resting-state functional MRI (rs-fMRI) data, we examined the effect of both time of day and the individual's chronotype on whole-brain network organization. In this regard, 62 participants (39 women; mean age: 23.

False Recognition in Short-Term Memory - Age-Differences in Confidence.

Compared to young adults, older adults are more susceptible to endorse false memories as genuine and exhibit higher confidence in their decisions to do so. While most studies to date have addressed this phenomenon in the context of episodic memory, the literature on age-differences in false recognition during short-term memory (STM) is scarce. Hence, the present study investigated age-related differences in the rate of false alarms (FA) and subsequent confidence judgments in STM.

Beyond the Low Frequency Fluctuations: Morning and Evening Differences in Human Brain.

Human performance, alertness, and most biological functions express rhythmic fluctuations across a 24-h-period. This phenomenon is believed to originate from differences in both circadian and homeostatic sleep-wake regulatory processes. Interactions between these processes result in time-of-day modulations of behavioral performance as well as brain activity patterns.

Saying "yes" when you want to say "no" - pupil dilation reflects evidence accumulation in a visual working memory recognition task.

A number of studies have shown that the pupil dilates during stimuli recognition and decision-making. Yet, little is known about the interaction between recognition memory and decision processes. Here, we investigated the possible link between pupil response and decision-related factors during a visual recognition task.

Would you say "yes" in the evening? Time-of-day effect on response bias in four types of working memory recognition tasks.

Across a wide range of tasks, cognitive functioning is affected by circadian fluctuations. In this study, we investigated diurnal variations of working memory performance, taking into account not only hits and errors rates, but also sensitivity (d') and response bias (c) indexes (established by signal detection theory). Fifty-two healthy volunteers performed four experimental tasks twice - in the morning and in the evening (approximately 1 and 10 h after awakening).

Different types of errors in saccadic task are sensitive to either time of day or chronic sleep restriction.

Circadian rhythms and restricted sleep length affect cognitive functions and, consequently, the performance of day to day activities. To date, no more than a few studies have explored the consequences of these factors on oculomotor behaviour. We have implemented a spatial cuing paradigm in an eye tracking experiment conducted four times of the day after one week of rested wakefulness and after one week of chronic partial sleep restriction.